This course shows how you to choose the right data types applicable to SQL Server developers and anyone who is responsible for designing and creating SQL Server tables and indexes, from SQL Server 2005 onward
This course is about how your database’s physical design either takes advantage of or is hindered by the way that the SQL Server platform works; knowing this can give you better long-term scalability, availability, and performance. Choosing the right data types when you're designing your columns, tables, and indexes is really critical. Using the wrong data type can cause more space to be required, affecting data density in memory, database and backup size, transaction log efficiency, and more. It's even more important when you're choosing your clustering keys, as the wrong choice there can cause nonclustered index sizes to balloon dramatically. It can even affect the performance of queries, when incompatible data types are used in comparisons and cause very costly operations to take place. This course will show you how to make the right choices and avoid all of these problems. It starts by explaining the various data structures that are used to store columns and rows, and how they can be affected by data type choice. Then it shows how data type choice factors into clustered and nonclustered index key choice. Finally it describes the implicit conversion and probe residual problems that can occur from mismatched data types used in queries. Packed with a wealth of information and practical, easy-to-follow demonstrations, this course will show you how to make the RIGHT choices to make sure you avoid all these common problems. The course is applicable for all SQL Server versions from SQL Server 2005 onward, and for SQL Server developers and anyone responsible for designing and creating SQL Server tables and indexes, with any level of experience.
Kimberly is a SQL Server MVP, Microsoft Regional Director and President/Founder of SQLskills.com, which she now runs with her husband, Paul Randal. Kimberly’s areas of expertise focus on performance tuning through effective database design and architecture.